Applied Machine Learning Applied Machine Learning Syllabus and logistics Siamak Ravanbakhsh Siamak Ravanbakhsh COMP 551 COMP 551 (winter 2020) (winter 2020) 1
Sections Sections Section one: Tuesday & Thursday, 11:30 am - 12:55 pm Location: Strathcona Anatomy & Dentistry M-1 Instructor: Reihaneh Rabbany <rrabba@cs.mcgill.ca> Office hours: Thursday, 1:30 pm - 2:30 pm @ MC 232 Website: http://www.reirab.com/comp55120.html Section two: Tuesday & Thursday, 4:30 pm - 5:30 pm Location: Maass Chemistry Building 10 Instructor: Siamak Ravanbakhsh <siamak@cs.mcgill.ca> Office hours : Wednesdays 4:30 pm-5:30 pm, ENGMC 325 0 0 1 Website: https://www.cs.mcgill.ca/~siamak/COMP551/index.html 2 . 1
Teaching Assistants Teaching Assistants Name Contact {@mail.mcgill.ca} Office hours Jin Dong jin.dong TBD Yanlin Zhang yanlin.zhang2 TBD Haque Ishfaq haque.ishfaq TBD Martin Klissarov martin.klissarov TBD Kian Ahrabian kian.ahrabian TBD Arnab Kumar Mondal arnab.mondal TBD Samin Yeasar Arnob samin.arnob TBD Tianzi Yang tianzi.yang TBD Zhilong Chen zhilong.chen TBD David Venuto david.venuto TBD 2 . 2
FAQ FAQ Will there be recordings ? No, but you can refer to the slides and assigned readings Will the two sections offer the same materials? That is the plan and assignments and mid-term will be jointly held, but the materials might or might not be covered in the same order, depth or pace. 2 . 3 Winter 2020 | Applied Machine Learning (COMP551)
About you! About you! 399 registered mostly undergraduates year 3 most have CS or CE background 3 . 1
About me About me Siamak Ravanbakhsh (pronounced almost like see-a-Mac) Assistant Professor in the School of Computer Science Canada CIFAR AI Chair and core member at Mila research interest: representation learning 0 what is the right representation for an AI agent? background in two approaches to this problem using probabilistic graphical models I also collaborate with physicists and cosmologists 3 . 2
About me About me Siamak Ravanbakhsh (pronounced almost like see-a-Mac) Assistant Professor in the School of Computer Science Canada CIFAR AI Chair and core member at Mila research interest: representation learning 0 what is the right representation for an AI agent? how do we learn quickly from data and perform inference background in two approaches to this problem using probabilistic graphical models I also collaborate with physicists and cosmologists 3 . 2
About me About me Siamak Ravanbakhsh (pronounced almost like see-a-Mac) Assistant Professor in the School of Computer Science Canada CIFAR AI Chair and core member at Mila research interest: representation learning 0 what is the right representation for an AI agent? how do we learn quickly from data and perform inference background in two approaches to this problem using probabilistic graphical models using invariances and symmetries I also collaborate with physicists and cosmologists 3 . 2
About them (TAs) About them (TAs) Name Jin Dong graph representation and NLP at Mila Yanlin Zhang computational biology Haque Ishfaq RL theory and bandits Martin Klissarov RL Kian Ahrabian software engineering and machine learning Arnab Kumar Mondal Samin Yeasar Arnob Tianzi Yang DL on computer vision and network 7 7 Zhilong Chen David Venuto Deep RL at Mila 3 . 3 Winter 2020 | Applied Machine Learning (COMP551)
About this course About this course Knowledge Knowledge Lectures Weekly Quizzes Midterm Skills Skills Hands-on Tutorials [optional] Mini-projects 4 . 1
About this course About this course complementary components complementary components Understand the theory behind learning algorithms Practice applying them in real-world 4 . 2
About this course About this course evaluation and grading evaluation and grading Weekly quizzes - 15% {online on Mondays} Midterm examination - 35% {written} Mini-projects - 50% {group assignments} 4 . 3
About this course About this course evaluation and grading evaluation and grading Weekly quizzes - 15% {online on Mondays} Midterm examination - 35% {written} March 30th 18:05-20:55 Let us know immidetly if you can not attend Mini-projects - 50% {group assignments} 4 . 4
Late submissions Late submissions All due dates are 11:59 pm in Montreal unless stated otherwise. No make-up quizzes will be given. 4 . 5
Prerequisites Prerequisites Python programming skills probability theory linear algebra calculus 4 . 6
Tutorials Tutorials {tentative and subject to change, exact dates TBD} 1 mid Jan. Python https://www.python.org/ 2 end of Jan. Scikit-learn https://scikit-learn.org/ 3 end of Feb. Pytorch https://pytorch.org/ No plan on tutorials on math but please fill out this poll , to see if there is enough demand for organizing one 4 . 7 Winter 2020 | Applied Machine Learning (COMP551)
Course outline Course outline This is very likely going to change during the semester Introduction Deep Learning Multilayer Perceptron Syllabus and Introduction Backpropagation K-Nearest Neighbours and Some Basic Concepts Convolutional Neural Networks Recurrent Neural Networks Classic Supervised Learning Linear Regression Unsupervised Learning Linear Classification Regularization, Bias-Variance Dimensionality Reduction Gradient Descent Clustering Support Vector Machines and Kernels Decision Trees Bayesian Inference Ensembles Bayesian Decision Theory Conjugate Priors Bayesian Linear Regression 5 . 1
Relevant Textbooks Relevant Textbooks No required textbook but slides will cover chapters from the following books, all available online, which can be used as reference materials. [Bishop] Pattern Recognition and Machine Learning by Christopher Bishop (2007) [HTF] The Elements of Statistical Learning : Data Mining, Inference, and Prediction (2009) by Trevor Hastie, Robert Tibshirani and Jerome Friedman [Murphy] Machine Learning: A Probabilistic Perspective by Kevin Murphy (2012), [GBC] Deep Learning (2016) by Ian Goodfellow, Yoshua Bengio, and Aaron Courville 5 . 2 Winter 2020 | Applied Machine Learning (COMP551)
Two pointers Two pointers 0 Course website Course website https://www.cs.mcgill.ca/~siamak/COMP551/index.html MyCourses MyCourses to check for announcements, form groups for projects, submit weekly quizzes, grades, discussions https://mycourses2.mcgill.ca/d2l/home/432032 6
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